Radiomics is an emerging computer vision technique that extracts high level textural information from medical images. Several studies have reported associations between radiomics features(RFs) and first pass effect(FPE) after mechanical thrombectomy(MT) treatment of acute ischemic stroke(AIS). However, the pathobiology behind the manifestation of such RFs remains unknown. To that end, we collected 15 clots samples retrieved from AIS patients (FPE: 5/15) treated with MT therapy along with their pre-treatment CT imaging (non-contrast CT–NCCT, and CT Angiography–CTA). We then segmented the clot regions on co-registered CT images and extracted 293 RFs, including. 1). shape-size metrics, 2). first-order statistics, and 3). higher order texture features. Univariate analysis was performed to test for significant differences in these RFs between FPE and non-FPE cases. Hematoxylin and eosin-stained clot sections from these cases were analyzed by Orbit Image Analysis software to determine if clot composition (i.e. % red blood cell-RBC, white blood cell-WBC, fibrin/platelets-FP) and structure (i.e. heterogeneity, organization) was significantly related to these RFs. Our results indicated that 5RFs, all from higher-order textural feature analysis, were significantly associated with FPE. These RFs were also associated with patient outcomes (delta NIHSS), albiet less significantly. There was no difference in RFs among clots of different composition (i.e. low vs high RBC clots), however, there were significant associations between the 5 RFs and clot organization parameters indicating that clots with ordered structures were easier to remove. These results need to be validated in larger datasets to establish the ability of RFs to predict FPE.
Background: Vascular segmentation of cerebral vascular imaging is tedious and manual, hindering translation of imagebased computational tools for neurovascular disease (such as intracranial aneurysm) management. Current cerebrovascular segmentation techniques use classic model-based algorithms, but such algorithms are incapable of distinguishing vasculature from artifacts. Deep Learning, specifically the widely accepted U-Net architecture, could be an effective alternative to conventional approaches for cerebrovascular segmentation, but has been shown to perform poorly in segmentation of smaller yet critical vessels. Methods: In this study, we present a methodology using a specialized convolutional neural network (CNN) architecture— DeepMedic—which uses multi-resolution inputs to enhance the field of view of the architecture, thereby enhancing the accuracy of segmentation of smaller vessels. To show the capability of this architecture, we collected and segmented a total of 100 digital subtraction angiography (DSA) images of cerebral vessels for training, internal validation, and testing (n=80, n=10, and n=10, respectively). Results: The DeepMedic architecture yielded high performance with a Connectivity-Area-Length (CAL) of 0.84±0.07 and a dice similarity coefficient (DSC) of 0.94±0.02 in the independent testing cohort. This was better than U-Net optimized for the patch-size and %-overlap in predictions, which performed with a CAL of 0.79±0.06 and a DSC of 0.92±0.02. Notably, our work demonstrated that DeepMedic (CAL: 0.45±0.12) outperformed U-Net (CAL: 0.59±0.11) for segmentation of smaller vessels. Conclusions: Our work showed DeepMedic performs better than the current state-of-the-art method for cerebrovascular segmentation. We hope this study begins to bring a high fidelity deep-learning based approach closer to clinical translation.
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